The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art.
A known technique for counting people includes separating a foreground and a background from an image based on motion information of an object, tracking an object that is determined to be a person from the separated foreground, and determining the number of tracked objects and directions thereof. This technique works acceptably with a low traffic or a simple background, but suffers from a decreased precision in a complicated environment with a high-traffic, dense population or in a system for monitoring a large area due to the effect of a large number of variables.
In addition, deficiencies of the conventional technique described above include (1) counting people is error-prone due to inaccurate foreground separation with shadow, occlusion or the like, (2) if there are many people passing through, difficulty of separating the objects hinders confirmation of whether or not region (or line) of interest is passed, resulting in failure of counting people, (3) with many people passing through, a large number of objects slows down the processing speed significantly, and (4) a camera lens generates a perspective distortion that wildly fluctuates depending on the camera and the surrounding environment, rendering it difficult to estimate the number of objects by regression.